A ``Dressed" Ensemble Kalman Filter Using the Hybrid  Coordinate Ocean Model in the Pacific

WAN Liying; ZHU Jiang; WANG Hui; YAN Changxiang; Laurent BERTINO

doi:10.1007/s00376-009-7208-6

Impact Factor: 5.8

Volume 26 Issue 5

Sep. 2009

Article Contents

Article > ADVANCES IN ATMOSPHERIC SCIENCES > 2009 > 26(5): 1042-1052

A ``Dressed" Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

1.
National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Mohn-Sverdrup Center/Nansen Environmental and Remote Sensing Center, Bergen N-5006, Norway

doi: 10.1007/s00376-009-7208-6

Abstract
References
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Abstract:
The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR). Ensemble optimal interpolation (EnOI), a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ``dressing' a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ``dressing' means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE) at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.
- Dressing Ensemble Kalman Filter (DrEnKF),
- HYbrid Coordinate Ocean Model,
- root mean square errors
References

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Manuscript History

Manuscript received: 10 September 2009

Manuscript revised: 10 September 2009

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

A ``Dressed" Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

1. National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,National Marine Environmental Forecasting Center, Beijing 100086,Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Mohn-Sverdrup Center/Nansen Environmental and Remote Sensing Center, Bergen N-5006, Norway

Manuscript received: 2009-09-10
Manuscript revised: 2009-09-10

Abstract: The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR). Ensemble optimal interpolation (EnOI), a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ``dressing' a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ``dressing' means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE) at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.

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